Department of Social and Decision SciencesCopyright (c) 2014 Carnegie Mellon University All rights reserved.http://repository.cmu.edu/sds
Recent documents in Department of Social and Decision Sciencesen-usThu, 09 Oct 2014 01:43:20 PDT3600Observed Variability and Values Matter: Toward a Better Understanding of Information Search and Decisions from Experiencehttp://repository.cmu.edu/sds/153
http://repository.cmu.edu/sds/153Tue, 07 Oct 2014 09:02:38 PDT
The search for different options before making a consequential choice is a central aspect of many important decisions, such as mate selection or purchasing a house. Despite its importance, surprisingly little is known about how search and choice are affected by the observed and objective properties of the decision problem. Here, we analyze the effects of two key properties in a binary choice task: the options' observed and objective values, and the variability of payoffs. First, in a large public data set of a binary choice task, we investigate how the observed value and variability relate to decision-makers' efforts and preferences during search. Furthermore, we test how these properties influence the chance of correctly identifying the objectively maximizing option, and how they affect choice. Second, we designed a novel experiment to systematically analyze the role of the objective difference between the options. We find that a larger objective difference between options increases the chance for correctly identifying the maximizing option, but it does not affect behavior during search and choice.
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Katja Mehlhorn et al.Developing trust: First impressions and experiencehttp://repository.cmu.edu/sds/152
http://repository.cmu.edu/sds/152Tue, 07 Oct 2014 09:02:36 PDT
Using the repeated Trust Game, we investigated how first impressions and experience affect trusting dispositions, beliefs, and behaviors. As in previous research, trusting beliefs and trust-related behaviors were greater at the start of the game for partners with trustworthy faces; and higher later in the game for partners who reciprocated. Three additional findings extended beyond the previous research. First, by measuring the discrete components of trusting beliefs rather than an umbrella “trustworthiness” measure, we confirmed that first impressions and experience influence judgments of competence, benevolence, and integrity. Moreover, we found suggestive evidence that perceptions of benevolence and integrity updated more quickly with experience than perceptions of competence. Second, by looking at trusting beliefs at the start of two consecutive repeated Trust Games, we found that judgments of competence, benevolence, and integrity continue to be influenced by trustworthy facial appearances, even after previous beliefs based on facial appearances were disconfirmed. Third, we found increased investment with a partner at the start of a second repeated Trust Game, even when participants expected their partners to betray them. Overall, our results clarify our understanding of how first impressions and experience influence trusting beliefs; provides evidence that changes in the repeated Trust Game represents learning about a specific partner rather than revisions of trusting dispositions; and highlights important distinctions between trusting beliefs and trust-related behaviors.
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Michael Yu et al.The boundaries of instance-based learning theory for explaining decisions from experience.http://repository.cmu.edu/sds/151
http://repository.cmu.edu/sds/151Tue, 07 Oct 2014 09:02:34 PDT
Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from description or experience), and although prospect theory explains decisions from description, a comprehensive model of decisions from experience is yet to be found. Instance-based learning theory (IBLT) explains how decisions are made from experience through interactions with dynamic environments (Gonzalez et al., 2003). The theory has shown robust explanations of behavior across multiple tasks and contexts, but it is becoming unclear what the theory is able to explain and what it does not. The goal of this chapter is to start addressing this problem. I will introduce IBLT and a recent cognitive model based on this theory: the IBL model of repeated binary choice; then I will discuss the phenomena that the IBL model explains and those that the model does not. The argument is for the theory's robustness but also for clarity in terms of concrete effects that the theory can or cannot account for.
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Cleotilde GonzalezAdaptation, PsychologicalDecision MakingEmotionsEnvironmentHumansIndividualityKnowledgeLearningPsychological TheoryRisk-TakingSocial BehaviorThe Description–Experience Gap in Risky and Ambiguous Gambleshttp://repository.cmu.edu/sds/150
http://repository.cmu.edu/sds/150Tue, 07 Oct 2014 09:02:33 PDT
Recent research in decision making reported a description–experience (DE) gap: opposite risky choices when decisions are made from descriptions (gambles in which probability distributions and outcomes are explicitly stated) and when decisions are made from experience (the knowledge of the gambles is obtained by sampling outcomes from unknown probability distributions before making a choice). The DE gap has been reported in gambles commonly involving a risky option (outcomes drawn from a fixed probability distribution) and a safe option (probability of the outcome is 1), or in gambles involving two risky options. Here, we extend the study of the DE gap to gambles in which people choose between a risky option and an ambiguous option (with two nested probability distributions, where the event-generation mechanism is more opaque than that in the risky option). We report empirical evidence and show a DE gap in gambles involving risky and ambiguous options. Participants' choices are influenced by the information format and by the ambiguous option: participants are ambiguity-seeking in experience and ambiguity-averse in description in problems involving both gains and losses. In order to find reasons for our results, we investigate participants' sampling behavior, and this analysis indicates choices according to a cognitive model of experiential decisions (instance-based learning). In experience, participants have small sample sizes, and participants choose options where high outcomes are experienced more frequently than expected. We discuss the implications of our results for the psychology of decision making in complex environments.
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Varun Dutt et al.Preparing for Novelty with Diverse Traininghttp://repository.cmu.edu/sds/149
http://repository.cmu.edu/sds/149Tue, 05 Nov 2013 13:49:39 PST
This study investigated the ability to generalize acquired skills from training conditions to novel conditions, in a complex perceptual and cognitive task of luggage screening. We examined category and exemplar diversity during training for preparing learners to detect novel items during transfer. Category diversity was manipulated in terms of heterogeneity of training categories: Participants either trained with targets from one category or with targets from several categories. Exemplar diversity was manipulated between participants by presenting either a few or many exemplars for both category diversity conditions. Seventy-two participants were trained to identify threats in pieces of luggage. Thereafter they were transferred to novel stimuli. Results can be summarized in support for the diversity of training hypothesis for preparing for novelty: To the best training for novel luggage screening situations is achieved using fewer items in a variety of categories.
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Angela Brunstein et al.Intergroup Prisoner’s Dilemma with Intragroup Power Dynamicshttp://repository.cmu.edu/sds/148
http://repository.cmu.edu/sds/148Tue, 05 Nov 2013 13:49:36 PST
The Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics (IPD^2) is a new game paradigm for studying human behavior in conflict situations. IPD^2 adds the concept of intragroup power to an intergroup version of the standard Repeated Prisoner’s Dilemma game. We conducted a laboratory study in which individual human participants played the game against computer strategies of various complexities. The results show that participants tend to cooperate more when they have greater power status within their groups. IPD^2 yields increasing levels of mutual cooperation and decreasing levels of mutual defection, in contrast to a variant of Intergroup Prisoner’s Dilemma without intragroup power dynamics where mutual cooperation and mutual defection are equally likely. We developed a cognitive model of human decision making in this game inspired by the Instance-Based Learning Theory (IBLT) and implemented within the ACT-R cognitive architecture. This model was run in place of a human participant using the same paradigm as the human study. The results from the model show a pattern of behavior similar to that of human data. We conclude with a discussion of the ways in which the IPD^2 paradigm can be applied to studying human behavior in conflict situations. In particular, we present the current study as a possible contribution to corroborating the conjecture that democracy reduces the risk of wars.
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Ion Juvina et al.Effects of feedback and complexity on repeated decisions from descriptionhttp://repository.cmu.edu/sds/147
http://repository.cmu.edu/sds/147Tue, 05 Nov 2013 13:49:33 PST

Recent evidence from research in risky choice shows that decisions from experience differ significantly from decisions from description, particularly when problems include a small-probability event and when samples from experience are small. Little is known, however, about the cognitive processes behind repeated decisions where both descriptive and experiential information are available for the decision maker. While previous findings suggest that feedback makes choices “deviate” from the predictions of prospect theory (Jessup, Bishara, & Busemeyer, 2008), we find a stronger effect: Our results suggest that information from description is neglected in the presence of feedback. Moreover, we find that in the presence of feedback, descriptions are overlooked irrespective of the level of complexity of the decision scenario. We show that an instance-based learning model and a reinforcement learning model account for the observed decisions by solely relying on observed outcomes. We discuss our findings in the context of organizational behavior.

Current understanding of sources of fatigue and of how fatigue affects performance in prolonged cognitive tasks is limited. We have observed that participants improve in response time but decrease in accuracy after extended repetitive work in a data entry task. We attributed the increase in errors to accumulating fatigue and the reduction in response time to learning. The concurrent effects of fatigue and learning seem intuitively reasonable but have not been explained computationally. This paper presents a cognitive computational model of these effects. The model, developed using the ACT-R cognitive architecture (1 and 3), accounts for learning and fatigue effects through a time-dependent modification of architectural parameters. The model is tested against human data from two independent experiments. Best fit to human accuracy and total response time was found from a modulation of both cognitive and arousal processes. Implications for training and skill acquisition research are discussed.

Methods: We summarize and illustrate several examples of dynamic decision-making research using simulations and microworlds as a starting point for a new theory of learning and skill acquisition in disaster triage. We describe MEDIC, a microworld in the context of medical diagnosis, and other simple tasks designed to gather people's understanding of accumulation, a basic component of dynamic tasks.

Results: Using a microworld called MEDIC, we demonstrate the difficulties of learning to be effective at medical decision making and present a set of theoretical constructs that help to explain those difficulties. Implications for how to overcome them are also discussed. On the basis of this kind of research and our instance-based learning theory, we develop principles for the design of effective disaster training and for building a theoretical framework that can systematically predict how to best train for successful performance in disaster situations. Finally, we also demonstrate the difficulty of understanding dynamic systems; educated adults with medical expertise have trouble understanding even simple dynamic medical problems.

Conclusions: Dynamic decision-making research can be used as a theoretical and empirical reference for advancing pediatric triage training to prepare trainees for disaster triage. Recommendations for effective learning derived from dynamic decision-making research are presented.
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Cleotilde Gonzalez et al.Measuring and Predicting Shared Situation Awareness in Teamshttp://repository.cmu.edu/sds/141
http://repository.cmu.edu/sds/141Tue, 05 Nov 2013 13:49:12 PST

In order to improve our understanding of situation awareness (SA) in teams performing in technologically advanced command, control, and communications (C3) operations, researchers need to develop valid approaches to assess both individual and shared SA. We investigated SA in an interdisciplinary military rescue operation training exercise. For this study, we developed procedures to measure the degree of shared SA between two team members and to improve the accuracy of their shared SA scores. We suggest that SA scores that are calculated using many existing methods may be inflated because they often fail to account for error in terms of both the amount of information that is thought to be relevant and in the accuracy of a person's knowledge of it. We calculated true SA scores that account for both of these types of error. The measures were then used to evaluate five potential predictors of shared SA. Our analysis suggested that failure to compensate for error in SA may lead to overestimation of performance in a situation. The results also revealed a significant relationship between shared SA and participants' distance from a central, joint service team, which acted as the organizational hub within the C3 structure. Shared SA was better the further away from the hub people were, which suggests that a person's role and position within an organization affects the level of shared SA that can be achieved with other individuals.

Reciprocity is common in economic and social domains, and it has been widely documented in the laboratory. While positive and negative reciprocity are observed in investment and ultimatum games, respectively, prior laboratory studies often neglect the effect of time delays that are common in real-world interactions. This research investigates the effect of time delays on reciprocity in the investment and ultimatum games. We manipulate the time delay after second movers have been informed about the first movers’ decisions. We find that a delay is correlated with fewer rejections in the ultimatum game, but we find no effect of delays in the investment game. A follow-up study explores some of the processes that occur during time delay in the ultimatum game. We find delays correlated to increased reported feelings of satisfaction and decreased reported feelings of disappointment. Increased satisfaction is correlated to an increased probability of rejection, while disappointment has a more complex relationship to the probability of rejection.

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Wei Siong Neo et al.Dissociation of S-R Compatibility and Simon Effects With Mixed Tasks and Mappingshttp://repository.cmu.edu/sds/139
http://repository.cmu.edu/sds/139Fri, 01 Nov 2013 13:43:09 PDT
Binary-choice reactions are typically faster when the stimulus location corresponds with that of the response than when it does not. This advantage of spatial correspondence is known as the stimulus-response compatibility (SRC) effect when the mapping of stimulus location, as the relevant stimulus dimension, is varied to be compatible or incompatible with response location. It is called the Simon effect when stimulus location is task-irrelevant. The SRC effect is eliminated when compatible and incompatible spatial mappings are mixed within a trial block, and the Simon effect is eliminated when the Simon task is mixed with the SRC task with incompatible spatial mapping. Eliminations of both types have been attributed to suppression of an automatic response-activation route. We tested predictions of this suppression hypothesis for conditions in which the SRC and Simon tasks were intermixed and the spatial mappings on the SRC trials could be compatible or incompatible. In Experiment 1, the two tasks were equally likely, as were compatible and incompatible spatial mappings on SRC trials; in Experiment 2, the SRC or Simon task was more frequent; and, in Experiment 3, the compatible or incompatible location mapping for the SRC task was more frequent. The SRC effect was absent overall in all experiments, whereas the Simon effect was robust to the manipulations and showed the characteristic decrease across the reaction time (RT) distribution. This dissociation of effects implies that the automatic response-activation route is not suppressed in mixed conditions and suggests that mixing influences the SRC and Simon effects by different means.
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Robert W. Proctor et al.Refuting data aggregation arguments and how the IBL model stands criticism: A reply to Hills and Hertwig (2012)http://repository.cmu.edu/sds/138
http://repository.cmu.edu/sds/138Fri, 01 Nov 2013 13:43:06 PDT
Hills and Hertwig (2012) challenge the proposed similarity of the exploration-exploitation transitions found in Gonzalez and Dutt (2011) between the two experimental paradigms of decisions from experience (sampling and repeated-choice), which was predicted by an Instance-Based Learning (IBL) model. The heart of their argument is that in the sampling paradigm, an impression of reduced exploration over time (alternation rate, A-rate) is produced by an inverse relationship between the sample size and the A-rate, and the aggregation of participants with different sample sizes. They suggest a normalization of the A-rate, which produces constant A-rate curves during sampling, and conclude with certain “ensuing problems for the IBL model.” We show that: the reduction of A-rate during sampling occurs even when sample length is controlled for; that regardless of the sampling length, the maximization behavior during sampling predicts the final choice; and that the IBL model accounts for a negative correlation between sample size and the predicted A-rate. Furthermore, when the IBL model's data is normalized following the procedure specified by Hills and Hertwig (2012), it results in similar flattened exploration curves as those found in human data. These results indicate that the transition from exploration to exploitation in the sampling paradigm (which has also been found in the repeated-choice paradigm) is not an illusion resulting from data aggregation: The same data with or without normalization may be interpreted differently, but such interpretations do not invalidate the mechanisms of the IBL model.
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Cleotilde Gonzalez et al.Action diversity in a simulation of the Israeli–Palestinian conflicthttp://repository.cmu.edu/sds/137
http://repository.cmu.edu/sds/137Fri, 01 Nov 2013 13:43:03 PDT

This article explores the strategies used by Israeli students to resolve the Israeli–Palestinian conflict in the interactive computer game, PeaceMaker. Students played PeaceMaker in the roles of both the Israeli Prime Minister and the Palestinian President in random order. Students must take actions satisfying constituents on both sides of the conflict in order to win the game. The diversity of actions taken in each role was measured. Several hypotheses test the degree to which Israeli students, depending on which role they played and their own demographic variables, exploited a consistent set of actions or explored a more diverse range of actions across three main types: construction, political, and security. The results show that (1) greater action diversity increases success in both roles, (2) Israeli students engaged in less diverse actions when playing the Israeli role than when playing the Palestinian role, (3) students’ religiosity and political Hawkishness negatively predicted action diversity when playing the Palestinian role, and (4) action diversity mediates the relationship between a student’s background knowledge about the conflict and success in the Israeli role. The significance of these findings for understanding attitudes about the Israeli–Palestinian conflict are discussed, including implications for conflict resolution more generally.

This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.

Research has shown widespread misconceptions in public understanding of the dynamics of climate change: A majority of people incorrectly infer that carbon-dioxide (CO2) concentrations can be controlled by stabilizing emissions at or above current rates (correlation heuristic), and while emissions continuously exceed absorptions (violation of mass balance). Such misconceptions are likely to delay actions that mitigate climate change. This paper tests a way to reduce these misconceptions through experience in a dynamic simulation. In a laboratory experiment, participants were randomly assigned to one of two conditions: description, where participants performed a CO2 stabilization (CS) task that provided them with a CO2 concentration trajectory over a 100 year period and asked them to sketch the corresponding CO2 emissions and absorptions over the same period; and experience, where participants performed the same task in a dynamic climate change simulator (DCCS), followed by the CS task. In both conditions, half of the participants were science and technology (STEM) majors, and the other half were non-STEM. Results revealed a significant reduction in people’s misconceptions in the experience condition compared to the description condition. Furthermore, STEMs demonstrated better performance than non-STEMs. These results highlight the potential for using experience-based simulation tools like DCCS to improve understanding about the dynamics of climate change.